This two legged robot could change how search and rescue operations are handled, with the ablity to enter areas too dangerous for humans.
AMOLF researchers and their collaborators from the Advanced Science Research Center (ASRC/CUNY) in New York have created a nanostructured surface capable of performing on-the-fly mathematical operations on an input image. This discovery could boost the speed of existing imaging processing techniques and lower energy usage. The work enables ultrafast object detection and augmented reality applications. The researchers publish their results today in the journal Nano Letters.
Image processing is at the core of several rapidly growing technologies, such as augmented reality, autonomous driving and more general object recognition. But how does a computer find and recognize an object? The initial step is to understand where its boundaries are, hence edge detection in an image becomes the starting point for image recognition. Edge detection is typically performed digitally using integrated electronic circuits implying fundamental speed limitations and high energy consumption, or in an analog fashion which requires bulky optics.
Extremists could generate ‘synthetic propaganda’, automatically creating white supremacist screeds, researchers warn.
The problem is the largest-ever GPT-2 model can also be fine-tuned for propaganda by extremist groups.
“Cogito, ergo sum,” Rene Descartes. Translation: “I think, therefore I am.”
What makes us, us? How is it that we’re able to look at a tree and see beauty, hear a song and feel moved, or take comfort in the smell of rain or the taste of coffee? How do we know we still exist when we close our eyes and lie in silence? To date, science doesn’t have an answer to those questions.
In fact, it doesn’t even have a unified theory. And that’s because we can’t simulate consciousness. All we can do is try to reverse-engineer it by studying living beings. Artificial intelligence, coupled with quantum computing, could solve this problem and provide the breakthrough insight scientists need to unravel the mysteries of consciousness. But first we need to take the solution seriously.
In the late ’90s, wildlife conservationists Zoe Jewell and Sky Alibhai were grappling with a troubling realization. The pair had been studying black rhino populations in Zimbabwe, and they spent a good deal of their time shooting the animals with tranquilizer darts and affixing radio collars around their necks. But after years of work, the researchers realized there was a major problem: Their technique, commonly used by all manner of wildlife scientists, seemed to be causing female rhinos to have fewer offspring.
The researchers published their findings in 2001, igniting a controversy in the conservation world. The problem, says Duke University professor of conservation ecology Stuart Pimm, is that being “collared” is extremely stressful for animals. “If you were walking through your neighborhood and suddenly a bunch of strange people came charging after you … and you got shot in the ass with a dart and woke up with something around your neck, I think you’d be in pretty bad shape too,” he says.
But Jewell and Alibhai had an idea. While working alongside the Shona tribe in Zimbabwe, they saw how the indigenous trackers were able to deduce an enormous amount of information about wildlife from animals’ footprints, including weight, sex, and species, all without getting anywhere close to the animals themselves. “We would go out with local game scouts, who were often expert trackers, and they would often laugh at us as we were listening to these signals coming from the collars,” Jewell says. “They would say to us, ‘all you need to do is look on the ground.”
DESI will map millions of galaxies and measure how fast they’re moving. This dataset can tell astronomers how much dark energy exists and how that’s changed.
Learning something new — and quickly — may depend on the lesson’s difficulty level, according to a new study.
Flipping the classroom, room temperature, and later school-day start times, are just a few of the countless interventions scientists have tested and some educators have implemented.
Now, scientists say they have cracked the code on the optimal level of difficulty to speed up learning. The team tested how the difficulty of training impacts the rate of learning in a broad class of learning algorithms, artificial neural networks, and computer models thought to simulate learning in humans and animals.
Too large to be classed as molecules, but too small to be bulk solids, atomic clusters can range in size from a few dozen to several hundred atoms. The structures can be used for a diverse range of applications, which requires a detailed knowledge of their shapes. These are easy to describe using mathematics in some cases; while in others, their morphologies are far more irregular. However, current models typically ignore this level of detail; often defining clusters as simple ball-shaped structures.
In research published in The European Physical Journal B, José M. Cabrera-Trujillo and colleagues at the Autonomous University of San Luis Potosí in Mexico propose a new method of identifying the morphologies of atomic clusters. They have now confirmed that the distinctive geometric shapes of some clusters, as well as the irregularity of amorphous structures, can be fully identified mathematically.
The insights gathered by Cabrera-Trujillo’s team could make it easier for researchers to engineer atomic clusters for specific applications. These could include nanoparticles containing two different metals, which are highly effective in catalysing chemical reactions. Their updated methods provided new ways to determine the structural properties of clusters, the ways in which they convert energy to different forms, and the potential forces between atoms. The technique was also able to distinguish the surrounding environments of atoms in the cores of clusters, and on their surfaces. Ultimately, this allowed the researchers to distinguish between distinctive shapes, including icosahedrons, octahedrons, and simple pancakes. They were also able to identify amorphous shapes, which contain no discernible mathematical order.
